DescriptionWhile machine learning is a key technology for autonomous driving, its application in an automotive environment poses severe constraints on processing power and energy consumption. This asks for combined hardware-software optimization approaches, maximally utilizing the available power budget and system capacity. Volkswagen AG and TU Dresden are working on solving this challenge, combining their expertise in automotive machine learning and energy-efficient hardware design. In this talk, we show specific methods for gaining more efficient deep neural network execution in a hardware-restricted environment and assess their potential in real-world examples. We demonstrate that a hardware-aware algorithm and network design boosts efficiency, thus bringing more effective processing power to the car.